Overview

Dataset statistics

Number of variables11
Number of observations19020
Missing cells0
Missing cells (%)0.0%
Duplicate rows115
Duplicate rows (%)0.6%
Total size in memory1.6 MiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 115 (0.6%) duplicate rowsDuplicates
fLength is highly overall correlated with fWidth and 3 other fieldsHigh correlation
fWidth is highly overall correlated with fLength and 3 other fieldsHigh correlation
fSize is highly overall correlated with fLength and 3 other fieldsHigh correlation
fConc is highly overall correlated with fLength and 3 other fieldsHigh correlation
fConc1 is highly overall correlated with fLength and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-07-18 14:42:08.251309
Analysis finished2023-07-18 14:42:55.434227
Duration47.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fLength
Real number (ℝ)

Distinct18643
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.250154
Minimum4.2835
Maximum334.177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:42:55.644006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.2835
5-th percentile16.433655
Q124.336
median37.1477
Q370.122175
95-th percentile139.72515
Maximum334.177
Range329.8935
Interquartile range (IQR)45.786175

Descriptive statistics

Standard deviation42.364855
Coefficient of variation (CV)0.79558183
Kurtosis4.9704412
Mean53.250154
Median Absolute Deviation (MAD)16.32565
Skewness2.0136523
Sum1012817.9
Variance1794.7809
MonotonicityNot monotonic
2023-07-18T14:42:55.963726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.7522 3
 
< 0.1%
24.8332 3
 
< 0.1%
26.9187 3
 
< 0.1%
19.1572 3
 
< 0.1%
12.9176 3
 
< 0.1%
98.2968 2
 
< 0.1%
32.2999 2
 
< 0.1%
84.5714 2
 
< 0.1%
24.8952 2
 
< 0.1%
12.4763 2
 
< 0.1%
Other values (18633) 18995
99.9%
ValueCountFrequency (%)
4.2835 1
< 0.1%
7.2079 1
< 0.1%
7.3606 1
< 0.1%
8.0518 1
< 0.1%
8.2304 1
< 0.1%
8.2311 1
< 0.1%
8.4802 1
< 0.1%
8.5738 1
< 0.1%
8.601 1
< 0.1%
8.6998 1
< 0.1%
ValueCountFrequency (%)
334.177 1
< 0.1%
310.61 1
< 0.1%
305.422 1
< 0.1%
305.324 1
< 0.1%
305.0961 1
< 0.1%
303.5676 1
< 0.1%
303.2787 1
< 0.1%
299.9304 1
< 0.1%
297.1239 1
< 0.1%
295.672 1
< 0.1%

fWidth
Real number (ℝ)

Distinct18200
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.180966
Minimum0
Maximum256.382
Zeros98
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:42:56.307339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4005
Q111.8638
median17.1399
Q324.739475
95-th percentile58.479245
Maximum256.382
Range256.382
Interquartile range (IQR)12.875675

Descriptive statistics

Standard deviation18.346056
Coefficient of variation (CV)0.82710808
Kurtosis16.765407
Mean22.180966
Median Absolute Deviation (MAD)5.87145
Skewness3.371628
Sum421881.98
Variance336.57778
MonotonicityNot monotonic
2023-07-18T14:42:56.663347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
 
0.5%
10.7539 4
 
< 0.1%
0.0001 3
 
< 0.1%
10.0342 3
 
< 0.1%
15.8644 3
 
< 0.1%
0.0029 3
 
< 0.1%
9.5513 3
 
< 0.1%
0.0033 3
 
< 0.1%
20.2021 3
 
< 0.1%
12.8155 3
 
< 0.1%
Other values (18190) 18894
99.3%
ValueCountFrequency (%)
0 98
0.5%
0.0001 3
 
< 0.1%
0.0002 1
 
< 0.1%
0.0006 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.0025 2
 
< 0.1%
0.0026 2
 
< 0.1%
0.0027 1
 
< 0.1%
0.0028 3
 
< 0.1%
0.0029 3
 
< 0.1%
ValueCountFrequency (%)
256.382 1
< 0.1%
228.0385 1
< 0.1%
220.5144 1
< 0.1%
201.364 1
< 0.1%
190.5432 1
< 0.1%
190.139 1
< 0.1%
188.8866 1
< 0.1%
186.928 1
< 0.1%
179.2924 1
< 0.1%
177.782 1
< 0.1%

fSize
Real number (ℝ)

Distinct7228
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.825017
Minimum1.9413
Maximum5.3233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:42:56.977888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.9413
5-th percentile2.1945
Q12.4771
median2.7396
Q33.1016
95-th percentile3.71575
Maximum5.3233
Range3.382
Interquartile range (IQR)0.6245

Descriptive statistics

Standard deviation0.47259865
Coefficient of variation (CV)0.16729055
Kurtosis0.72727844
Mean2.825017
Median Absolute Deviation (MAD)0.29895
Skewness0.87550717
Sum53731.823
Variance0.22334948
MonotonicityNot monotonic
2023-07-18T14:42:57.300791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1508 27
 
0.1%
2.1287 24
 
0.1%
2.0774 24
 
0.1%
2.1319 23
 
0.1%
2.1414 22
 
0.1%
2.3139 22
 
0.1%
2.1351 22
 
0.1%
2.3936 21
 
0.1%
2.29 21
 
0.1%
2.3589 20
 
0.1%
Other values (7218) 18794
98.8%
ValueCountFrequency (%)
1.9413 1
 
< 0.1%
1.9468 1
 
< 0.1%
1.9916 1
 
< 0.1%
1.9978 1
 
< 0.1%
2.0022 1
 
< 0.1%
2.0065 2
 
< 0.1%
2.0107 3
 
< 0.1%
2.0149 4
< 0.1%
2.0191 1
 
< 0.1%
2.0233 8
< 0.1%
ValueCountFrequency (%)
5.3233 1
< 0.1%
5.1795 1
< 0.1%
5.1467 1
< 0.1%
5.0118 1
< 0.1%
5.01 1
< 0.1%
4.9946 1
< 0.1%
4.9518 1
< 0.1%
4.9369 1
< 0.1%
4.905 1
< 0.1%
4.8501 1
< 0.1%

fConc
Real number (ℝ)

Distinct6410
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38032707
Minimum0.0131
Maximum0.893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:42:57.632058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0131
5-th percentile0.1263
Q10.2358
median0.35415
Q30.5037
95-th percentile0.734205
Maximum0.893
Range0.8799
Interquartile range (IQR)0.2679

Descriptive statistics

Standard deviation0.18281315
Coefficient of variation (CV)0.48067351
Kurtosis-0.5212971
Mean0.38032707
Median Absolute Deviation (MAD)0.13025
Skewness0.48588845
Sum7233.8209
Variance0.033420647
MonotonicityNot monotonic
2023-07-18T14:42:57.956033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 16
 
0.1%
0.4 12
 
0.1%
0.4116 12
 
0.1%
0.2979 12
 
0.1%
0.2175 11
 
0.1%
0.2214 11
 
0.1%
0.5 11
 
0.1%
0.6154 11
 
0.1%
0.193 11
 
0.1%
0.2408 11
 
0.1%
Other values (6400) 18902
99.4%
ValueCountFrequency (%)
0.0131 1
< 0.1%
0.0133 1
< 0.1%
0.0137 1
< 0.1%
0.0139 2
< 0.1%
0.0158 1
< 0.1%
0.0162 1
< 0.1%
0.0171 1
< 0.1%
0.0188 1
< 0.1%
0.0196 1
< 0.1%
0.0206 1
< 0.1%
ValueCountFrequency (%)
0.893 1
< 0.1%
0.8912 1
< 0.1%
0.8889 1
< 0.1%
0.8846 1
< 0.1%
0.8786 1
< 0.1%
0.8778 1
< 0.1%
0.8772 1
< 0.1%
0.8757 1
< 0.1%
0.8745 1
< 0.1%
0.8743 1
< 0.1%

fConc1
Real number (ℝ)

Distinct4421
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21465713
Minimum0.0003
Maximum0.6752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:42:58.277014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.066995
Q10.128475
median0.1965
Q30.285225
95-th percentile0.42241
Maximum0.6752
Range0.6749
Interquartile range (IQR)0.15675

Descriptive statistics

Standard deviation0.1105108
Coefficient of variation (CV)0.51482472
Kurtosis0.029391024
Mean0.21465713
Median Absolute Deviation (MAD)0.0754
Skewness0.68569463
Sum4082.7787
Variance0.012212637
MonotonicityNot monotonic
2023-07-18T14:42:58.625959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.194 18
 
0.1%
0.2126 16
 
0.1%
0.1939 16
 
0.1%
0.2 16
 
0.1%
0.217 15
 
0.1%
0.2251 15
 
0.1%
0.1515 14
 
0.1%
0.1504 14
 
0.1%
0.1279 14
 
0.1%
0.1568 14
 
0.1%
Other values (4411) 18868
99.2%
ValueCountFrequency (%)
0.0003 1
< 0.1%
0.0008 1
< 0.1%
0.0011 1
< 0.1%
0.0015 1
< 0.1%
0.002 1
< 0.1%
0.0047 1
< 0.1%
0.005 1
< 0.1%
0.0072 1
< 0.1%
0.0073 1
< 0.1%
0.0076 1
< 0.1%
ValueCountFrequency (%)
0.6752 1
< 0.1%
0.674 1
< 0.1%
0.643 1
< 0.1%
0.637 1
< 0.1%
0.6296 1
< 0.1%
0.6283 1
< 0.1%
0.6264 1
< 0.1%
0.6242 1
< 0.1%
0.6224 1
< 0.1%
0.6204 1
< 0.1%

fAsym
Real number (ℝ)

Distinct18704
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3317452
Minimum-457.9161
Maximum575.2407
Zeros41
Zeros (%)0.2%
Negative8448
Negative (%)44.4%
Memory size148.7 KiB
2023-07-18T14:42:58.971339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-457.9161
5-th percentile-111.1947
Q1-20.58655
median4.01305
Q324.0637
95-th percentile65.544125
Maximum575.2407
Range1033.1568
Interquartile range (IQR)44.65025

Descriptive statistics

Standard deviation59.206062
Coefficient of variation (CV)-13.667947
Kurtosis8.1553298
Mean-4.3317452
Median Absolute Deviation (MAD)21.68065
Skewness-1.0464415
Sum-82389.793
Variance3505.3578
MonotonicityNot monotonic
2023-07-18T14:42:59.319809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
0.2%
-0.0001 7
 
< 0.1%
8.8077 3
 
< 0.1%
7.1088 3
 
< 0.1%
-1.4761 3
 
< 0.1%
-0.5062 3
 
< 0.1%
15 2
 
< 0.1%
36.6631 2
 
< 0.1%
-2.0651 2
 
< 0.1%
58.6184 2
 
< 0.1%
Other values (18694) 18952
99.6%
ValueCountFrequency (%)
-457.9161 1
< 0.1%
-449.9526 1
< 0.1%
-382.594 1
< 0.1%
-381.734 1
< 0.1%
-378.9457 1
< 0.1%
-368.633 1
< 0.1%
-363.3382 1
< 0.1%
-353.934 1
< 0.1%
-353.26 1
< 0.1%
-349.757 1
< 0.1%
ValueCountFrequency (%)
575.2407 1
< 0.1%
473.0654 1
< 0.1%
464.631 1
< 0.1%
444.401 1
< 0.1%
433.0957 1
< 0.1%
402.925 1
< 0.1%
402.1863 1
< 0.1%
400.284 1
< 0.1%
396.3379 1
< 0.1%
384.3477 1
< 0.1%

fM3Long
Real number (ℝ)

Distinct18693
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.545545
Minimum-331.78
Maximum238.321
Zeros39
Zeros (%)0.2%
Negative6604
Negative (%)34.7%
Memory size148.7 KiB
2023-07-18T14:43:01.194031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-331.78
5-th percentile-80.28369
Q1-12.842775
median15.3141
Q335.8378
95-th percentile83.07177
Maximum238.321
Range570.101
Interquartile range (IQR)48.680575

Descriptive statistics

Standard deviation51.000118
Coefficient of variation (CV)4.8361767
Kurtosis4.6709738
Mean10.545545
Median Absolute Deviation (MAD)25.33365
Skewness-1.1230781
Sum200576.26
Variance2601.012
MonotonicityNot monotonic
2023-07-18T14:43:01.748490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
 
0.2%
-0.0001 4
 
< 0.1%
16.0747 3
 
< 0.1%
-10.7301 2
 
< 0.1%
20.1723 2
 
< 0.1%
-18.5409 2
 
< 0.1%
54.47 2
 
< 0.1%
22.649 2
 
< 0.1%
-18.2535 2
 
< 0.1%
14.9656 2
 
< 0.1%
Other values (18683) 18960
99.7%
ValueCountFrequency (%)
-331.78 1
< 0.1%
-318.3002 1
< 0.1%
-297.1717 1
< 0.1%
-293.1762 1
< 0.1%
-287.5067 1
< 0.1%
-287.3636 1
< 0.1%
-284.7038 1
< 0.1%
-281.9541 1
< 0.1%
-281.844 1
< 0.1%
-281.435 1
< 0.1%
ValueCountFrequency (%)
238.321 1
< 0.1%
231.446 1
< 0.1%
227.8174 1
< 0.1%
226.3506 1
< 0.1%
222.417 1
< 0.1%
217.934 1
< 0.1%
217.624 1
< 0.1%
216.985 1
< 0.1%
215.894 1
< 0.1%
203.863 1
< 0.1%

fM3Trans
Real number (ℝ)

Distinct18390
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24972596
Minimum-205.8947
Maximum179.851
Zeros59
Zeros (%)0.3%
Negative9404
Negative (%)49.4%
Memory size148.7 KiB
2023-07-18T14:43:02.254650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-205.8947
5-th percentile-25.76384
Q1-10.849375
median0.6662
Q310.946425
95-th percentile26.99851
Maximum179.851
Range385.7457
Interquartile range (IQR)21.7958

Descriptive statistics

Standard deviation20.827439
Coefficient of variation (CV)83.401178
Kurtosis8.5803525
Mean0.24972596
Median Absolute Deviation (MAD)10.888
Skewness0.12012127
Sum4749.7877
Variance433.78221
MonotonicityNot monotonic
2023-07-18T14:43:02.764767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
0.3%
-0.0001 24
 
0.1%
0.0001 18
 
0.1%
-5.4454 3
 
< 0.1%
-7.6601 3
 
< 0.1%
11.1602 3
 
< 0.1%
6.1829 3
 
< 0.1%
-8.975 3
 
< 0.1%
10.9015 3
 
< 0.1%
9.5231 3
 
< 0.1%
Other values (18380) 18898
99.4%
ValueCountFrequency (%)
-205.8947 1
< 0.1%
-164.14 1
< 0.1%
-149.5513 1
< 0.1%
-142.5894 1
< 0.1%
-142.119 1
< 0.1%
-135.5051 1
< 0.1%
-134.75 1
< 0.1%
-134.395 1
< 0.1%
-133.1359 1
< 0.1%
-132.416 1
< 0.1%
ValueCountFrequency (%)
179.851 1
< 0.1%
170.692 1
< 0.1%
163.2697 1
< 0.1%
154.865 1
< 0.1%
143.8753 1
< 0.1%
139.2361 1
< 0.1%
132.589 1
< 0.1%
132.388 1
< 0.1%
131.5547 1
< 0.1%
130.8545 1
< 0.1%

fAlpha
Real number (ℝ)

Distinct17981
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.645707
Minimum0
Maximum90
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:43:03.337710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.933285
Q15.547925
median17.6795
Q345.88355
95-th percentile80.72654
Maximum90
Range90
Interquartile range (IQR)40.335625

Descriptive statistics

Standard deviation26.103621
Coefficient of variation (CV)0.94421969
Kurtosis-0.5337036
Mean27.645707
Median Absolute Deviation (MAD)14.6924
Skewness0.85088988
Sum525821.34
Variance681.399
MonotonicityNot monotonic
2023-07-18T14:43:03.897505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0002 7
 
< 0.1%
0 5
 
< 0.1%
0.386 4
 
< 0.1%
1.29 4
 
< 0.1%
90 4
 
< 0.1%
0.804 4
 
< 0.1%
0.256 4
 
< 0.1%
3.4161 4
 
< 0.1%
2.76 4
 
< 0.1%
2.701 4
 
< 0.1%
Other values (17971) 18976
99.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.0002 7
< 0.1%
0.0003 2
 
< 0.1%
0.001 1
 
< 0.1%
0.0031 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.0086 1
 
< 0.1%
0.009 1
 
< 0.1%
0.0097 1
 
< 0.1%
0.0103 1
 
< 0.1%
ValueCountFrequency (%)
90 4
< 0.1%
89.9798 1
 
< 0.1%
89.9579 1
 
< 0.1%
89.9535 1
 
< 0.1%
89.9528 1
 
< 0.1%
89.9229 1
 
< 0.1%
89.9155 1
 
< 0.1%
89.9087 1
 
< 0.1%
89.9076 1
 
< 0.1%
89.9042 1
 
< 0.1%

fDist
Real number (ℝ)

Distinct18437
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.81803
Minimum1.2826
Maximum495.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.7 KiB
2023-07-18T14:43:04.495709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.2826
5-th percentile71.41369
Q1142.49225
median191.85145
Q3240.56383
95-th percentile326.65997
Maximum495.561
Range494.2784
Interquartile range (IQR)98.071575

Descriptive statistics

Standard deviation74.731787
Coefficient of variation (CV)0.38557707
Kurtosis-0.11257659
Mean193.81803
Median Absolute Deviation (MAD)49.0165
Skewness0.22958738
Sum3686418.9
Variance5584.84
MonotonicityNot monotonic
2023-07-18T14:43:04.876018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182.013 3
 
< 0.1%
227.107 3
 
< 0.1%
265.238 3
 
< 0.1%
146.354 3
 
< 0.1%
186.828 3
 
< 0.1%
168.774 3
 
< 0.1%
216.032 3
 
< 0.1%
187.651 3
 
< 0.1%
148.372 3
 
< 0.1%
100.395 3
 
< 0.1%
Other values (18427) 18990
99.8%
ValueCountFrequency (%)
1.2826 1
< 0.1%
5.5449 1
< 0.1%
5.5922 1
< 0.1%
5.6998 1
< 0.1%
5.7456 1
< 0.1%
6.564 1
< 0.1%
6.6852 1
< 0.1%
9.1574 1
< 0.1%
13.1108 1
< 0.1%
14.0229 1
< 0.1%
ValueCountFrequency (%)
495.561 1
< 0.1%
466.4078 1
< 0.1%
450.953 1
< 0.1%
450.402 1
< 0.1%
450.349 1
< 0.1%
448.0295 1
< 0.1%
446.488 1
< 0.1%
438.901 1
< 0.1%
438.8574 1
< 0.1%
437.477 1
< 0.1%

class
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.7 KiB
0
12332 
1
6688 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19020
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Length

2023-07-18T14:43:05.199012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T14:43:05.505965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Most occurring characters

ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19020
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Most occurring scripts

ValueCountFrequency (%)
Common 19020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12332
64.8%
1 6688
35.2%

Interactions

2023-07-18T14:42:51.572741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:10.967944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:14.441513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:18.380431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:21.511498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:25.884536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:34.453294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:37.320566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:39.856570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:45.930075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:51.838425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:11.257660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:14.807147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:18.653403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:21.917236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:26.376677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:34.930352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:37.580866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:40.256104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:46.831734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:52.092854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:11.522646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:15.184759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:18.918318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:22.266051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:27.115507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:35.244020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:37.828781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:40.727538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:47.752054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:52.386784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:11.796967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:15.797921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:19.208328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:22.940691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:27.697476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:35.502602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:38.108377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:41.268062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:48.626118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:52.655812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:12.068951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:16.202668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:19.477030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:23.277041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:28.472316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:35.746769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:38.353669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:41.673139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:49.409938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:53.255814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:12.377075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:16.635029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:19.784834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:23.662084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:29.544686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:36.045039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:38.626091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:42.178976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:49.820019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:53.531533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:12.781051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:17.058693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:20.081202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:24.170474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:30.310519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:36.291707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:38.867976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:42.656963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:50.306658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:53.787256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:13.212241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:17.510965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:20.371776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:24.520380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:31.423366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:36.539358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:39.131562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:43.189092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:50.652898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:54.060103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:13.620481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:17.848868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:20.656353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:24.885571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:32.334124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:36.802558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:39.385734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:44.073994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:51.045759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:54.305755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:14.017091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:18.122120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:21.032867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:25.271949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:33.890522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:37.068034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:39.617280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:44.899395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T14:42:51.306137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-18T14:43:05.711949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDistclass
fLength1.0000.7530.834-0.829-0.804-0.1000.2970.004-0.2510.4810.362
fWidth0.7531.0000.838-0.854-0.831-0.1000.2080.013-0.1990.3780.323
fSize0.8340.8381.000-0.912-0.885-0.0350.3240.011-0.2880.4230.141
fConc-0.829-0.854-0.9121.0000.9860.015-0.315-0.0120.287-0.3530.147
fConc1-0.804-0.831-0.8850.9861.0000.005-0.307-0.0110.281-0.3360.139
fAsym-0.100-0.100-0.0350.0150.0051.0000.328-0.005-0.075-0.1560.263
fM3Long0.2970.2080.324-0.315-0.3070.3281.000-0.002-0.2570.1920.342
fM3Trans0.0040.0130.011-0.012-0.011-0.005-0.0021.000-0.0000.0050.283
fAlpha-0.251-0.199-0.2880.2870.281-0.075-0.257-0.0001.000-0.2760.475
fDist0.4810.3780.423-0.353-0.336-0.1560.1920.005-0.2761.0000.126
class0.3620.3230.1410.1470.1390.2630.3420.2830.4750.1261.000

Missing values

2023-07-18T14:42:54.719314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-18T14:42:55.182033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDistclass
028.796716.00212.64490.39180.198227.700422.0110-8.202740.092081.88280
131.603611.72352.51850.53030.377326.272223.8238-9.95746.3609205.26100
2162.0520136.03104.06120.03740.0187116.7410-64.8580-45.216076.9600256.78800
323.81729.57282.33850.61470.392227.2107-6.4633-7.151310.4490116.73700
475.136230.92053.16110.31680.1832-5.527728.552521.83934.6480356.46200
551.624021.15022.90850.24200.134050.876143.18879.81453.6130238.09800
648.246817.35653.03320.25290.15158.573038.095710.58684.7920219.08700
726.789713.75952.55210.42360.217429.633920.4560-2.92920.8120237.13400
896.232746.51654.15400.07790.0390110.355085.048643.18444.8540248.22600
946.761915.19932.57860.33770.191324.754843.8771-6.68127.8750102.25100
fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDistclass
1901032.490210.67232.47420.46640.2735-27.0097-21.16878.481369.1730120.66801
1901179.552844.99293.54880.16560.0900-39.621353.7866-30.005415.8075311.56801
1901231.837313.87342.82510.41690.1988-16.4919-27.144811.109811.3663100.05661
19013182.500376.55683.68720.11230.0666192.267593.0302-62.619282.1691283.47311
1901443.298017.35452.83070.28770.1646-60.1842-33.8513-3.654578.4099224.82991
1901521.384610.91702.61610.58570.393415.261811.52452.87662.4229106.82581
1901628.94526.70202.26720.53510.278437.081613.1853-2.963286.7975247.45601
1901775.445547.53053.44830.14170.0549-9.356141.0562-9.466230.2987256.51661
19018120.513576.90183.99390.09440.06835.8043-93.5224-63.838984.6874408.31661
19019187.181453.00143.20930.28760.1539-167.3125-168.455831.475552.7310272.31741

Duplicate rows

Most frequently occurring

fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDistclass# duplicates
012.917611.35962.11230.74130.390015.0388-5.6768-11.563864.9330227.107012
112.980110.88152.41750.74570.4723-13.69706.0371-7.001930.803078.261812
213.028710.95442.20000.75710.4511-14.09855.7807-10.174864.8700182.980012
314.791211.79552.30750.67490.45571.35334.7675-9.061162.250062.524512
416.756611.30632.37660.58400.35500.00000.15436.741948.5040117.636012
516.989411.00022.45640.62940.3514-3.49028.0823-7.051655.393091.376112
618.434317.87172.38470.48660.2701-15.7044-16.5170-12.231171.0730158.703012
718.49149.76352.48290.65790.3734-1.80607.65206.726033.8161188.867012
818.809011.13052.54960.60370.42450.5645-3.060811.817675.5740222.591012
919.084813.73462.58490.60080.396617.282419.4696-5.23905.3161213.714012